Prior art automobile collision avoidance systems commonly depend upon Radio Detection and Ranging (“RADAR”) or Light Detection and Ranging (“LIDAR”) to detect and determine object range and azimuth of a foreign object relative to a host vehicle. The commercial use of these two sensors is currently limited to a narrow field of view in advance of the automobile. Preferred comprehensive collision avoidance is 360-degree awareness of objects, moving or stationary, and prior art discloses RADAR and LIDAR approaches to 360-degree coverage.
The potential disadvantages of 360-degree RADAR and LIDAR are expense, and the emission of energy into the environment. The emission of energy would become a problem when many systems simultaneously attempt to probe the environment and mutually interfere, as should be expected if automatic collision avoidance becomes popular. Lower frequency, longer wavelength radio frequency (RF) sensors such as RADAR suffer additionally from lower range and azimuth resolution, and lower update rates compared to the requirements for 360-degree automobile collision avoidance. Phased-array RADAR could potentially overcome some of the limitations of conventional rotating antenna RADAR but is as yet prohibitively expensive for commercial automobile applications.
Visible light sensors offer greater resolution than lower frequency RADAR, but this potential is dependent upon adequate sensor focal plane pixel density and adequate image processing capabilities. The focal plane is the sensor's receptor surface upon which an image is focused by a lens. Prior art passive machine vision systems used in collision avoidance systems do not emit energy and thus avoid the problem of interference, although object-emitted or reflected light is still required. Passive vision systems are also relatively inexpensive compared to RADAR and LIDAR, but single camera systems have the disadvantage of range indeterminacy and a relatively narrow field of view. However, there is but one and only one trajectory of an object in the external volume sensed by two cameras that generates any specific pattern set in the two cameras simultaneously. Thus, binocular registration of images can be used to de-confound object range and azimuth.
Multiple camera systems in sufficient quantity can provide 360-degree coverage of the host vehicle's environment and, with overlapping fields of view can provide information necessary to determine range. U.S. Patent Application Publication No. 2004/0246333 discloses such a configuration. However, the required and available vision analyses for range determination from stereo pairs of cameras depend upon solutions to the correspondence problem. The correspondence problem is a difficulty in identifying the points on one focal plane projection from one camera that correspond to the points on another focal plane projection from another camera.
One common approach to solving the correspondence problem is statistical, in which multiple analyses of the feature space are made to find the strongest correlations of features between the two projections. The statistical approach is computationally expensive for a two camera system. This expense would only be multiplied by the number of cameras required for 360-degree coverage. Camera motion and object motion offer additional challenges to the determination of depth from stereo machine vision as object image features and focal plane projection locations are changing over time. In collision avoidance, however, the relative movement of objects is a key consideration, and thus should figure principally in the selection of objects of interest for the assessment of collision risk, and in the determination of avoidance maneuvers. A machine vision system based on motion analysis from an array of overlapping high-pixel density vision sensors, could thus directly provide the most relevant information, and could simplify the computations required to assess the ranges, azimuths, elevations, and behaviors of objects, both moving and stationary about a moving host vehicle.
The present subject matter overcomes all of the above disadvantages of prior art by providing an inexpensive means for accurate object location determination for 360 degrees about a host vehicle using a machine vision system composed of an array of overlapping vision sensors and visual motion-based object detection, ranging, and avoidance.